Overview

Dataset statistics

Number of variables15
Number of observations457
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.3 KiB
Average record size in memory177.6 B

Variable types

Numeric14
Categorical1

Warnings

TotalSteps is highly correlated with TotalDistance and 7 other fieldsHigh correlation
TotalDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
VeryActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 2 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with TotalSteps and 4 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with TotalSteps and 3 other fieldsHigh correlation
Calories is highly correlated with TotalSteps and 3 other fieldsHigh correlation
TotalSteps is highly correlated with TotalDistance and 8 other fieldsHigh correlation
TotalDistance is highly correlated with TotalSteps and 8 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 8 other fieldsHigh correlation
VeryActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 6 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with TotalSteps and 5 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with TotalSteps and 6 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with TotalSteps and 3 other fieldsHigh correlation
Calories is highly correlated with TotalSteps and 2 other fieldsHigh correlation
TotalSteps is highly correlated with TotalDistance and 7 other fieldsHigh correlation
TotalDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
VeryActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with TotalSteps and 5 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with TotalSteps and 5 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with TotalSteps and 3 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with ModeratelyActiveDistanceHigh correlation
VeryActiveMinutes is highly correlated with TotalSteps and 4 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with Calories and 7 other fieldsHigh correlation
Calories is highly correlated with LightlyActiveMinutes and 6 other fieldsHigh correlation
LightActiveDistance is highly correlated with LightlyActiveMinutes and 5 other fieldsHigh correlation
SedentaryMinutes is highly correlated with LightlyActiveMinutes and 5 other fieldsHigh correlation
LoggedActivitiesDistance is highly correlated with LightlyActiveMinutes and 1 other fieldsHigh correlation
TotalSteps is highly correlated with VeryActiveMinutes and 9 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with FairlyActiveMinutes and 3 other fieldsHigh correlation
TotalDistance is highly correlated with VeryActiveMinutes and 9 other fieldsHigh correlation
VeryActiveDistance is highly correlated with VeryActiveMinutes and 5 other fieldsHigh correlation
ActivityDate is highly correlated with CaloriesHigh correlation
TrackerDistance is highly correlated with VeryActiveMinutes and 9 other fieldsHigh correlation
SedentaryActiveDistance is highly correlated with LoggedActivitiesDistanceHigh correlation
Id is highly correlated with VeryActiveMinutes and 6 other fieldsHigh correlation
TotalSteps has 61 (13.3%) zeros Zeros
TotalDistance has 63 (13.8%) zeros Zeros
TrackerDistance has 66 (14.4%) zeros Zeros
LoggedActivitiesDistance has 433 (94.7%) zeros Zeros
VeryActiveDistance has 245 (53.6%) zeros Zeros
ModeratelyActiveDistance has 228 (49.9%) zeros Zeros
LightActiveDistance has 74 (16.2%) zeros Zeros
SedentaryActiveDistance has 419 (91.7%) zeros Zeros
VeryActiveMinutes has 241 (52.7%) zeros Zeros
FairlyActiveMinutes has 227 (49.7%) zeros Zeros
LightlyActiveMinutes has 72 (15.8%) zeros Zeros
Calories has 5 (1.1%) zeros Zeros

Reproduction

Analysis started2021-09-12 03:39:43.671503
Analysis finished2021-09-12 03:40:45.228119
Duration1 minute and 1.56 second
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct35
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4628594643
Minimum1503960366
Maximum8877689391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:45.407572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1503960366
5-th percentile1624580081
Q12347167796
median4057192912
Q36391747486
95-th percentile8792009665
Maximum8877689391
Range7373729025
Interquartile range (IQR)4044579690

Descriptive statistics

Standard deviation2293781430
Coefficient of variation (CV)0.4955675764
Kurtosis-1.039194515
Mean4628594643
Median Absolute Deviation (MAD)2030840877
Skewness0.3527246238
Sum2.115267752 × 1012
Variance5.261433247 × 1018
MonotonicityIncreasing
2021-09-12T09:10:45.689041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
405719291232
 
7.0%
402033265032
 
7.0%
162458008119
 
4.2%
150396036619
 
4.2%
470292168415
 
3.3%
234716779615
 
3.3%
444511498615
 
3.3%
696218106714
 
3.1%
825324287912
 
2.6%
455860992412
 
2.6%
Other values (25)272
59.5%
ValueCountFrequency (%)
150396036619
4.2%
162458008119
4.2%
164443008110
2.2%
184450507212
2.6%
192797227912
2.6%
202248440812
2.6%
202635203512
2.6%
232012700212
2.6%
234716779615
3.3%
287321276512
2.6%
ValueCountFrequency (%)
887768939112
2.6%
879200966512
2.6%
85838150598
1.8%
837856320012
2.6%
825324287912
2.6%
805347532811
2.4%
708636192612
2.6%
700774417112
2.6%
696218106714
3.1%
67758889559
2.0%

ActivityDate
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size29.3 KiB
4/4/2016
35 
4/5/2016
35 
4/2/2016
35 
4/3/2016
35 
4/1/2016
34 
Other values (27)
283 

Length

Max length9
Median length8
Mean length8.332603939
Min length8

Characters and Unicode

Total characters3808
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3/25/2016
2nd row3/26/2016
3rd row3/27/2016
4th row3/28/2016
5th row3/29/2016

Common Values

ValueCountFrequency (%)
4/4/201635
 
7.7%
4/5/201635
 
7.7%
4/2/201635
 
7.7%
4/3/201635
 
7.7%
4/1/201634
 
7.4%
4/8/201633
 
7.2%
4/6/201633
 
7.2%
4/7/201633
 
7.2%
4/9/201632
 
7.0%
4/10/201629
 
6.3%
Other values (22)123
26.9%

Length

2021-09-12T09:10:46.277710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4/4/201635
 
7.7%
4/5/201635
 
7.7%
4/2/201635
 
7.7%
4/3/201635
 
7.7%
4/1/201634
 
7.4%
4/8/201633
 
7.2%
4/6/201633
 
7.2%
4/7/201633
 
7.2%
4/9/201632
 
7.0%
4/10/201629
 
6.3%
Other values (22)123
26.9%

Most occurring characters

ValueCountFrequency (%)
/914
24.0%
1623
16.4%
2556
14.6%
0499
13.1%
6496
13.0%
4422
11.1%
3135
 
3.5%
944
 
1.2%
541
 
1.1%
739
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2894
76.0%
Other Punctuation914
 
24.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1623
21.5%
2556
19.2%
0499
17.2%
6496
17.1%
4422
14.6%
3135
 
4.7%
944
 
1.5%
541
 
1.4%
739
 
1.3%
839
 
1.3%
Other Punctuation
ValueCountFrequency (%)
/914
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/914
24.0%
1623
16.4%
2556
14.6%
0499
13.1%
6496
13.0%
4422
11.1%
3135
 
3.5%
944
 
1.2%
541
 
1.1%
739
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/914
24.0%
1623
16.4%
2556
14.6%
0499
13.1%
6496
13.0%
4422
11.1%
3135
 
3.5%
944
 
1.2%
541
 
1.1%
739
 
1.0%

TotalSteps
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct389
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6546.562363
Minimum0
Maximum28497
Zeros61
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:46.542590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11988
median5986
Q310198
95-th percentile15605.6
Maximum28497
Range28497
Interquartile range (IQR)8210

Descriptive statistics

Standard deviation5398.493064
Coefficient of variation (CV)0.824630205
Kurtosis0.6648241126
Mean6546.562363
Median Absolute Deviation (MAD)4120
Skewness0.803413395
Sum2991779
Variance29143727.36
MonotonicityNot monotonic
2021-09-12T09:10:46.892162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061
 
13.3%
41952
 
0.4%
44992
 
0.4%
55432
 
0.4%
63442
 
0.4%
20982
 
0.4%
124092
 
0.4%
82
 
0.4%
72
 
0.4%
68351
 
0.2%
Other values (379)379
82.9%
ValueCountFrequency (%)
061
13.3%
41
 
0.2%
72
 
0.4%
82
 
0.4%
141
 
0.2%
181
 
0.2%
201
 
0.2%
241
 
0.2%
441
 
0.2%
871
 
0.2%
ValueCountFrequency (%)
284971
0.2%
275721
0.2%
257011
0.2%
241361
0.2%
230141
0.2%
207791
0.2%
202371
0.2%
201881
0.2%
196581
0.2%
189521
0.2%

TotalDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct334
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.663522972
Minimum0
Maximum27.53000069
Zeros63
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:47.572754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.409999967
median4.090000153
Q37.159999847
95-th percentile11.23999977
Maximum27.53000069
Range27.53000069
Interquartile range (IQR)5.749999881

Descriptive statistics

Standard deviation4.082072268
Coefficient of variation (CV)0.8753194296
Kurtosis3.448976394
Mean4.663522972
Median Absolute Deviation (MAD)2.889999866
Skewness1.321383526
Sum2131.229998
Variance16.663314
MonotonicityNot monotonic
2021-09-12T09:10:47.994936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063
 
13.8%
0.0099999997766
 
1.3%
7.6700000763
 
0.7%
4.719999793
 
0.7%
1.9199999572
 
0.4%
8.1199998862
 
0.4%
4.780000212
 
0.4%
1.5499999522
 
0.4%
0.82999998332
 
0.4%
5.3299999242
 
0.4%
Other values (324)370
81.0%
ValueCountFrequency (%)
063
13.8%
0.0099999997766
 
1.3%
0.019999999552
 
0.4%
0.029999999331
 
0.2%
0.10999999941
 
0.2%
0.12999999521
 
0.2%
0.14000000062
 
0.4%
0.18999999761
 
0.2%
0.20999999341
 
0.2%
0.25999999052
 
0.4%
ValueCountFrequency (%)
27.530000691
0.2%
23.389999391
0.2%
20.909999851
0.2%
20.389999391
0.2%
20.139999391
0.2%
18.409999851
0.2%
15.819999691
0.2%
15.619999891
0.2%
14.840000151
0.2%
14.710000041
0.2%

TrackerDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct336
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.609846824
Minimum0
Maximum27.53000069
Zeros66
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:48.432577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.279999971
median4.090000153
Q37.110000134
95-th percentile11.13599968
Maximum27.53000069
Range27.53000069
Interquartile range (IQR)5.830000162

Descriptive statistics

Standard deviation4.068539937
Coefficient of variation (CV)0.8825759494
Kurtosis3.576888152
Mean4.609846824
Median Absolute Deviation (MAD)2.920000076
Skewness1.339050019
Sum2106.699999
Variance16.55301722
MonotonicityNot monotonic
2021-09-12T09:10:48.751740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
066
 
14.4%
0.0099999997766
 
1.3%
7.6700000763
 
0.7%
4.719999793
 
0.7%
1.9199999572
 
0.4%
1.5499999522
 
0.4%
5.3299999242
 
0.4%
0.82999998332
 
0.4%
6.9800000192
 
0.4%
1.4299999482
 
0.4%
Other values (326)367
80.3%
ValueCountFrequency (%)
066
14.4%
0.0099999997766
 
1.3%
0.019999999552
 
0.4%
0.029999999331
 
0.2%
0.10999999941
 
0.2%
0.12999999521
 
0.2%
0.14000000062
 
0.4%
0.18999999761
 
0.2%
0.20999999341
 
0.2%
0.25999999052
 
0.4%
ValueCountFrequency (%)
27.530000691
0.2%
23.389999391
0.2%
20.909999851
0.2%
20.389999391
0.2%
20.139999391
0.2%
18.409999851
0.2%
15.819999691
0.2%
15.619999891
0.2%
14.840000151
0.2%
14.710000041
0.2%

LoggedActivitiesDistance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1794273741
Minimum0
Maximum6.72705698
Zeros433
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:49.077549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3665432006
Maximum6.72705698
Range6.72705698
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8492318298
Coefficient of variation (CV)4.733011526
Kurtosis27.13615724
Mean0.1794273741
Median Absolute Deviation (MAD)0
Skewness5.159788142
Sum81.99830997
Variance0.7211947008
MonotonicityNot monotonic
2021-09-12T09:10:49.344395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0433
94.7%
2.0921471124
 
0.9%
2.2530810833
 
0.7%
4.8363800051
 
0.2%
2.6964550021
 
0.2%
5.456863881
 
0.2%
3.2186880111
 
0.2%
3.972795011
 
0.2%
6.727056981
 
0.2%
0.055842999371
 
0.2%
Other values (10)10
 
2.2%
ValueCountFrequency (%)
0433
94.7%
0.055842999371
 
0.2%
1.6093440061
 
0.2%
1.9263019561
 
0.2%
2.0277729031
 
0.2%
2.0921471124
 
0.9%
2.2530810833
 
0.7%
2.6964550021
 
0.2%
3.2186880111
 
0.2%
3.972795011
 
0.2%
ValueCountFrequency (%)
6.727056981
0.2%
5.456863881
0.2%
5.1898498541
0.2%
4.9012827871
0.2%
4.8759899141
0.2%
4.8697829251
0.2%
4.8423199651
0.2%
4.8363800051
0.2%
4.8357200621
0.2%
4.8280320171
0.2%

VeryActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct170
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.180897153
Minimum0
Maximum21.92000008
Zeros245
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:49.698216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.309999943
95-th percentile5.760000038
Maximum21.92000008
Range21.92000008
Interquartile range (IQR)1.309999943

Descriptive statistics

Standard deviation2.487158568
Coefficient of variation (CV)2.106160186
Kurtosis18.7096635
Mean1.180897153
Median Absolute Deviation (MAD)0
Skewness3.73064421
Sum539.669999
Variance6.185957745
MonotonicityNot monotonic
2021-09-12T09:10:50.047017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0245
53.6%
0.07000000036
 
1.3%
0.254
 
0.9%
0.23000000423
 
0.7%
0.53
 
0.7%
0.33000001313
 
0.7%
0.10000000152
 
0.4%
2.0199999812
 
0.4%
0.87000000482
 
0.4%
1.7999999522
 
0.4%
Other values (160)185
40.5%
ValueCountFrequency (%)
0245
53.6%
0.0099999997761
 
0.2%
0.019999999551
 
0.2%
0.039999999111
 
0.2%
0.059999998662
 
0.4%
0.07000000036
 
1.3%
0.079999998212
 
0.4%
0.090000003581
 
0.2%
0.10000000152
 
0.4%
0.10999999941
 
0.2%
ValueCountFrequency (%)
21.920000081
0.2%
16.819999691
0.2%
14.720000271
0.2%
12.220000271
0.2%
12.060000421
0.2%
11.729999541
0.2%
11.100000381
0.2%
9.9799995421
0.2%
9.9700002671
0.2%
9.9600000381
0.2%

ModeratelyActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct140
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4786433252
Minimum0
Maximum6.400000095
Zeros228
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:50.376832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01999999955
Q30.6700000167
95-th percentile2.089999914
Maximum6.400000095
Range6.400000095
Interquartile range (IQR)0.6700000167

Descriptive statistics

Standard deviation0.8309951707
Coefficient of variation (CV)1.736146995
Kurtosis11.87056988
Mean0.4786433252
Median Absolute Deviation (MAD)0.01999999955
Skewness2.971412313
Sum218.7399996
Variance0.6905529737
MonotonicityNot monotonic
2021-09-12T09:10:50.704643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0228
49.9%
0.255
 
1.1%
0.18000000725
 
1.1%
0.33000001314
 
0.9%
0.52999997144
 
0.9%
0.23000000424
 
0.9%
0.15999999644
 
0.9%
0.25999999054
 
0.9%
0.37000000484
 
0.9%
0.21999999884
 
0.9%
Other values (130)191
41.8%
ValueCountFrequency (%)
0228
49.9%
0.019999999551
 
0.2%
0.039999999112
 
0.4%
0.050000000753
 
0.7%
0.059999998661
 
0.2%
0.090000003581
 
0.2%
0.10000000151
 
0.2%
0.11999999732
 
0.4%
0.12999999521
 
0.2%
0.14000000061
 
0.2%
ValueCountFrequency (%)
6.4000000951
0.2%
5.4899997711
0.2%
4.4899997711
0.2%
4.4400000571
0.2%
3.7200000291
0.2%
3.6800000671
0.2%
3.5999999051
0.2%
3.3399999141
0.2%
3.259999991
0.2%
3.1199998861
0.2%

LightActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct295
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.890196937
Minimum0
Maximum12.51000023
Zeros74
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:51.049442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8700000048
median2.930000067
Q34.460000038
95-th percentile6.515999985
Maximum12.51000023
Range12.51000023
Interquartile range (IQR)3.590000033

Descriptive statistics

Standard deviation2.237523344
Coefficient of variation (CV)0.7741767752
Kurtosis0.2750757813
Mean2.890196937
Median Absolute Deviation (MAD)1.749999762
Skewness0.5215831453
Sum1320.82
Variance5.006510715
MonotonicityNot monotonic
2021-09-12T09:10:51.408233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
074
 
16.2%
0.0099999997767
 
1.5%
4.6100001344
 
0.9%
3.54
 
0.9%
3.509999993
 
0.7%
3.9100000863
 
0.7%
5.2699999813
 
0.7%
4.9499998093
 
0.7%
3.2100000383
 
0.7%
1.6299999953
 
0.7%
Other values (285)350
76.6%
ValueCountFrequency (%)
074
16.2%
0.0099999997767
 
1.5%
0.019999999551
 
0.2%
0.029999999331
 
0.2%
0.10999999941
 
0.2%
0.12999999522
 
0.4%
0.14000000061
 
0.2%
0.17000000181
 
0.2%
0.18999999761
 
0.2%
0.20999999342
 
0.4%
ValueCountFrequency (%)
12.510000231
0.2%
121
0.2%
9.3699998861
0.2%
8.6199998861
0.2%
8.1499996191
0.2%
8.0799999241
0.2%
8.060000421
0.2%
8.0200004581
0.2%
7.7899999621
0.2%
7.5599999431
0.2%

SedentaryActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001903719882
Minimum0
Maximum0.1000000015
Zeros419
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:51.682079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.009999999776
Maximum0.1000000015
Range0.1000000015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0084868013
Coefficient of variation (CV)4.45800949
Kurtosis54.03881246
Mean0.001903719882
Median Absolute Deviation (MAD)0
Skewness6.566301549
Sum0.8699999861
Variance7.202579631 × 10-5
MonotonicityNot monotonic
2021-09-12T09:10:51.916940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0419
91.7%
0.00999999977622
 
4.8%
0.029999999336
 
1.3%
0.039999999114
 
0.9%
0.019999999552
 
0.4%
0.059999998662
 
0.4%
0.10000000151
 
0.2%
0.050000000751
 
0.2%
ValueCountFrequency (%)
0419
91.7%
0.00999999977622
 
4.8%
0.019999999552
 
0.4%
0.029999999336
 
1.3%
0.039999999114
 
0.9%
0.050000000751
 
0.2%
0.059999998662
 
0.4%
0.10000000151
 
0.2%
ValueCountFrequency (%)
0.10000000151
 
0.2%
0.059999998662
 
0.4%
0.050000000751
 
0.2%
0.039999999114
 
0.9%
0.029999999336
 
1.3%
0.019999999552
 
0.4%
0.00999999977622
 
4.8%
0419
91.7%

VeryActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct85
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.62363239
Minimum0
Maximum202
Zeros241
Zeros (%)52.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:52.216766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile78.4
Maximum202
Range202
Interquartile range (IQR)25

Descriptive statistics

Standard deviation28.91970375
Coefficient of variation (CV)1.739674163
Kurtosis6.928585794
Mean16.62363239
Median Absolute Deviation (MAD)0
Skewness2.38473603
Sum7597
Variance836.3492648
MonotonicityNot monotonic
2021-09-12T09:10:52.545586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0241
52.7%
111
 
2.4%
39
 
2.0%
48
 
1.8%
27
 
1.5%
77
 
1.5%
157
 
1.5%
187
 
1.5%
56
 
1.3%
256
 
1.3%
Other values (75)148
32.4%
ValueCountFrequency (%)
0241
52.7%
111
 
2.4%
27
 
1.5%
39
 
2.0%
48
 
1.8%
56
 
1.3%
63
 
0.7%
77
 
1.5%
84
 
0.9%
94
 
0.9%
ValueCountFrequency (%)
2021
0.2%
1651
0.2%
1281
0.2%
1241
0.2%
1231
0.2%
1161
0.2%
1131
0.2%
1071
0.2%
1061
0.2%
1051
0.2%

FairlyActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.07002188
Minimum0
Maximum660
Zeros227
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:52.840413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q316
95-th percentile46.4
Maximum660
Range660
Interquartile range (IQR)16

Descriptive statistics

Standard deviation36.20863518
Coefficient of variation (CV)2.770357656
Kurtosis224.7286191
Mean13.07002188
Median Absolute Deviation (MAD)1
Skewness13.02940846
Sum5973
Variance1311.065262
MonotonicityNot monotonic
2021-09-12T09:10:53.130291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0227
49.7%
614
 
3.1%
1612
 
2.6%
811
 
2.4%
710
 
2.2%
119
 
2.0%
179
 
2.0%
129
 
2.0%
98
 
1.8%
158
 
1.8%
Other values (52)140
30.6%
ValueCountFrequency (%)
0227
49.7%
13
 
0.7%
23
 
0.7%
33
 
0.7%
47
 
1.5%
55
 
1.1%
614
 
3.1%
710
 
2.2%
811
 
2.4%
98
 
1.8%
ValueCountFrequency (%)
6601
0.2%
1411
0.2%
1331
0.2%
1201
0.2%
1141
0.2%
1071
0.2%
1011
0.2%
991
0.2%
812
0.4%
771
0.2%

LightlyActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct251
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.0700219
Minimum0
Maximum720
Zeros72
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:53.421153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q164
median181
Q3257
95-th percentile348.6
Maximum720
Range720
Interquartile range (IQR)193

Descriptive statistics

Standard deviation122.2053721
Coefficient of variation (CV)0.7185591605
Kurtosis0.3822726388
Mean170.0700219
Median Absolute Deviation (MAD)90
Skewness0.3525141968
Sum77722
Variance14934.15298
MonotonicityNot monotonic
2021-09-12T09:10:53.713004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
072
 
15.8%
16
 
1.3%
2485
 
1.1%
2084
 
0.9%
2124
 
0.9%
2304
 
0.9%
2764
 
0.9%
1903
 
0.7%
2723
 
0.7%
2633
 
0.7%
Other values (241)349
76.4%
ValueCountFrequency (%)
072
15.8%
16
 
1.3%
23
 
0.7%
31
 
0.2%
61
 
0.2%
82
 
0.4%
91
 
0.2%
111
 
0.2%
121
 
0.2%
141
 
0.2%
ValueCountFrequency (%)
7201
0.2%
6301
0.2%
5861
0.2%
5061
0.2%
4911
0.2%
4751
0.2%
4221
0.2%
4011
0.2%
3971
0.2%
3901
0.2%

SedentaryMinutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct315
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean995.2822757
Minimum32
Maximum1440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:54.013066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile465.4
Q1728
median1057
Q31285
95-th percentile1440
Maximum1440
Range1408
Interquartile range (IQR)557

Descriptive statistics

Standard deviation337.021404
Coefficient of variation (CV)0.3386189146
Kurtosis-0.6782227116
Mean995.2822757
Median Absolute Deviation (MAD)300
Skewness-0.3655631139
Sum454844
Variance113583.4267
MonotonicityNot monotonic
2021-09-12T09:10:54.344873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144063
 
13.8%
14394
 
0.9%
11814
 
0.9%
13283
 
0.7%
11253
 
0.7%
7703
 
0.7%
11503
 
0.7%
11853
 
0.7%
8423
 
0.7%
7003
 
0.7%
Other values (305)365
79.9%
ValueCountFrequency (%)
321
0.2%
611
0.2%
751
0.2%
991
0.2%
1461
0.2%
1611
0.2%
1871
0.2%
1981
0.2%
2071
0.2%
2091
0.2%
ValueCountFrequency (%)
144063
13.8%
14394
 
0.9%
14382
 
0.4%
14321
 
0.2%
14281
 
0.2%
14141
 
0.2%
14071
 
0.2%
14062
 
0.4%
14041
 
0.2%
13991
 
0.2%

Calories
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct383
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2189.452954
Minimum0
Maximum4562
Zeros5
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2021-09-12T09:10:54.673534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile790.8
Q11776
median2062
Q32667
95-th percentile3716
Maximum4562
Range4562
Interquartile range (IQR)891

Descriptive statistics

Standard deviation815.4845229
Coefficient of variation (CV)0.3724603999
Kurtosis0.5200100199
Mean2189.452954
Median Absolute Deviation (MAD)422
Skewness0.2363575209
Sum1000580
Variance665015.0071
MonotonicityNot monotonic
2021-09-12T09:10:55.007340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177611
 
2.4%
18788
 
1.8%
20606
 
1.3%
05
 
1.1%
18205
 
1.1%
14295
 
1.1%
19205
 
1.1%
13244
 
0.9%
19353
 
0.7%
17773
 
0.7%
Other values (373)402
88.0%
ValueCountFrequency (%)
05
1.1%
501
 
0.2%
1821
 
0.2%
2511
 
0.2%
3992
 
0.4%
4461
 
0.2%
4891
 
0.2%
5381
 
0.2%
6001
 
0.2%
6251
 
0.2%
ValueCountFrequency (%)
45621
0.2%
45261
0.2%
44301
0.2%
42861
0.2%
42341
0.2%
42201
0.2%
41961
0.2%
41281
0.2%
40391
0.2%
40341
0.2%

Interactions

2021-09-12T09:09:48.308825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:49.454161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:49.795962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:50.121791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:50.538551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:50.867342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:51.135190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:51.448005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:51.766825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:52.054658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:52.322500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:52.587345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:52.901168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:53.176008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:53.410872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:53.671722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:53.896590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:54.123457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:54.374314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:54.617172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:54.809062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:55.030936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:55.248808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:55.500662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:55.760512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:55.992379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:56.236557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:56.474099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:56.687975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:56.907848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:57.258646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:57.459528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:57.682403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:57.901273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:58.109154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:58.332024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:58.550898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:58.779765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:59.015629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:59.230506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:59.444380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:59.671249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:09:59.864154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:00.108000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:00.354854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:00.612705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:00.860562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:01.178378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:01.396254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:01.633115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:02.106857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:02.491621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:02.860017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:03.157849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:03.436686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:03.791484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:04.088309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:04.415123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:04.707492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:05.140242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:05.480388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:05.796205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:06.094050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:06.587793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:06.910909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:07.304569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:07.695395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:08.027340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:08.362150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:08.711300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:08.968150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:09.208026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:09.426899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:09.650770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:09.872640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:10.128514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:10.336404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:10.568275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:10.811645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:11.083352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:11.349206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:11.605571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:11.836441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:12.073319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:12.303200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:12.569559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:12.807441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:13.053289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:13.293164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:13.559544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:13.798406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:14.050288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:14.320644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:14.593487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:14.880329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:15.137179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:15.415533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:15.689380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:15.933245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:16.223598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:16.508443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:16.763303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:17.018163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:17.520419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:17.773288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:18.040125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:18.285490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:18.539366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:18.823209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:19.079570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:19.357446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:19.622302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:19.855197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:20.113053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:20.353444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:20.598331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:20.840228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:21.091089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:21.318473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:21.586329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:21.851173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:22.101030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:22.389113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:22.700956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:22.972819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:23.230675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:23.471563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:23.770122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:24.040977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:24.329838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:24.614683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:24.916041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:25.204904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:25.488298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:25.779130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:26.068962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:26.395773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:26.689605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:26.992430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:27.292256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:27.565098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:27.840314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:28.085174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:28.332030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:28.594179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:28.863027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:29.109885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:29.370757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:29.633631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:29.927466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:30.213309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:30.746028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:31.017426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:31.411236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:31.766039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:32.132351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:32.440192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:32.770005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:33.117339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:33.443173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:33.754529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:34.076357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:34.400196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:34.732008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:35.069321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:35.379154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:35.716960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:36.080272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:36.370111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:36.694443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:37.007280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:37.252144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:37.502000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:37.786365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:38.036232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:38.322071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:38.607448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:38.877324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:39.236137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:39.572458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:39.904266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:40.236105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:40.481974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:40.718349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:40.926237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:41.142138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:41.396982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:41.644353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:41.862638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:42.117497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:42.356364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:42.598236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:42.835119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:43.072006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:43.298401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T09:10:43.536039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-09-12T09:10:55.324160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-12T09:10:55.926834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-12T09:10:56.492557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-12T09:10:57.071239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-12T09:10:43.962800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-12T09:10:44.842324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
015039603663/25/2016110047.117.110.02.570.464.070.033122058041819
115039603663/26/20161760911.5511.550.06.920.733.910.089172745882154
215039603663/27/2016127368.538.530.04.660.163.710.05652686051944
315039603663/28/2016132318.938.930.03.190.794.950.0392022410801932
415039603663/29/2016120417.857.850.02.161.094.610.028282437631886
515039603663/30/2016109707.167.160.02.360.514.290.0301322311741820
615039603663/31/2016122567.867.860.02.290.495.040.033122398201889
715039603664/1/2016122627.877.870.03.320.833.640.047212008661868
815039603664/2/2016112487.257.250.03.000.453.740.040112446361843
915039603664/3/2016100166.376.370.00.911.284.180.015303146551850

Last rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
44788776893914/3/2016152608.1900008.1900000.01.800.755.570.001061725910583864
44888776893914/4/20162077918.41000018.4100000.011.730.656.000.00781620811383662
44988776893914/5/2016106958.1200008.1200000.00.770.187.090.0110324611812834
45088776893914/6/20162413620.91000020.9100000.012.220.548.080.00871631810194039
45188776893914/7/2016109108.4200008.4200000.02.960.395.030.00321121211852947
45288776893914/8/20162301420.38999920.3899990.011.100.638.620.0070293599824196
45388776893914/9/2016164708.0700008.0700000.00.000.028.020.0090928910523841
45488776893914/10/20162849727.53000127.5300010.021.921.124.460.001284621110554526
45588776893914/11/2016106228.0600008.0600000.01.470.156.370.0118722511902820
45688776893914/12/201623501.7800001.7800000.00.000.001.780.000058531938